Automated detection of unusual events on stairs

Jasper Snoek, Jesse Hoey, Liam Stewart, Richard S. Zemel, Alex Mihailidis

    Research output: Contribution to journalArticle

    30 Citations (Scopus)

    Abstract

    This paper presents a method for automatically detecting unusual human events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find the events of interest within large quantities of video data. Our system identifies potential sequences containing anomalies, and reduces the amount of data that needs to be searched by a human. We compute two sets of features from a video of a person descending a stairwell. The first set of features are the foot positions and velocities. We track both feet using a mixed state particle filter with an appearance model based on histograms of oriented gradients. We compute expected (most likely) foot positions given the state of the filter at each frame. The second set of features are the parameters of the mean optical flow over a foreground region. Our final classification system inputs these two sets of features into a hidden Markov model (HMM) to analyse the spatio-temporal progression of the stair descent. A single HMM is trained on sequences of normal stair use, and a threshold on sequence likelihoods is used to detect unusual events in new data. We demonstrate our system on a data set with five people descending a set of stairs in a laboratory environment. We show how our system can successfully detect nearly all anomalous events, with a low false positive rate. We discuss limitations and suggest improvements to the system. (C) 2008 Elsevier B.V. All rights reserved.

    Original languageEnglish
    Pages (from-to)153-166
    Number of pages14
    JournalImage and Vision Computing
    Volume27
    Issue number1-2
    DOIs
    Publication statusPublished - 1 Jan 2009

    Cite this

    Snoek, J., Hoey, J., Stewart, L., Zemel, R. S., & Mihailidis, A. (2009). Automated detection of unusual events on stairs. Image and Vision Computing, 27(1-2), 153-166. https://doi.org/10.1016/j.imavis.2008.04.021